Re: st: Log Transform Justification

In my PhD project, I was having problem to have a good model with
Thikonov Regularization in inverse problem. I checked the data and
try to get a good distribution of the data set. I did log
transform of raw data and run the model with transformed data. The
model that I get is fantastic. Now I am happy to put the model,
however, I think I have to justify the data transformation. So how
can I justify?

First, understand that you have not provided enough information for
anyone to give a specific answer to your question. The most general
answer, of course, is that the justification is this: this is the
model that fits the data. Models that clearly don't fit the data are
not useful (at least from a data-analytic perspective).

In my experience, reviewers/readers who are bothered by a
transformation of the data come from two main groups. The first are
those who are having difficulty interpreting the results of the model
(this is by far the largest group). For those, translating your
results back onto the original scale is usually sufficient (though be
careful how you do this). The second are people who genuinely have a
problem with the model because it appears to violate some theoretical
expectation (these are typically individuals from a mathematical
discipline such as physics or economics). In this case, you may be
obligated to demonstrate that the model on the untransformed data
does not fit well, either graphically or via a formal test. At that
point, one of two things must be true: either the theory is wrong, or
your data are bad. Either way, this gets you off the topic of your
analysis and into other questions.